




import numpy as np
import torch
import tqdm

from ...models.unet_ad import UNetaDModel
from ...pipelines import DiffusionPipeline
from ...utils import randn_tensor
from ...utils.dummy_pt_objects import DDPMScheduler


class 							UpperCamelCase_       (					UpperCAmelCase__	):

    '''simple docstring'''






    def __init__(     self :      Any   ,					UpperCAmelCase__ :      UNetaDModel   ,					UpperCAmelCase__ :      UNetaDModel   ,					UpperCAmelCase__ :      DDPMScheduler   ,					UpperCAmelCase__ :      Optional[int]   ,					)		->int:





           '''simple docstring'''

           super().__init__()
           A__ 					=       value_function
           A__ 					=       unet
           A__ 					=       scheduler
           A__ 					=       env
           A__ 					=       env.get_dataset()
           A__ 					=       {}
           for key in self.data.keys():
                  try:
                         A__ 					=       self.data[key].mean()
                  except:  # noqa: E722
                         pass
           A__ 					=       {}
           for key in self.data.keys():
                  try:
                         A__ 					=       self.data[key].std()
                  except:  # noqa: E722
                         pass
           A__ 					=       env.observation_space.shape[0]
           A__ 					=       env.action_space.shape[0]


    def 			SCREAMING_SNAKE_CASE				(     self :      Any   ,					UpperCAmelCase__ :      str   ,					UpperCAmelCase__ :      int)		->List[str]:





           '''simple docstring'''

           return (x_in - self.means[key]) / self.stds[key]


    def 			SCREAMING_SNAKE_CASE				(     self :      int   ,					UpperCAmelCase__ :      str   ,					UpperCAmelCase__ :      List[Any])		->Any:





           '''simple docstring'''

           return x_in * self.stds[key] + self.means[key]


    def 			SCREAMING_SNAKE_CASE				(     self :      Dict   ,					UpperCAmelCase__ :      Union[str, Any])		->Dict:





           '''simple docstring'''

           if type(UpperCAmelCase__) is dict:
                  return {k: self.to_torch(UpperCAmelCase__) for k, v in x_in.items()}
           elif torch.is_tensor(UpperCAmelCase__):
                  return x_in.to(self.unet.device)
           return torch.tensor(UpperCAmelCase__   ,					device=self.unet.device)


    def 			SCREAMING_SNAKE_CASE				(     self :      List[str]   ,					UpperCAmelCase__ :      List[Any]   ,					UpperCAmelCase__ :      Optional[Any]   ,					UpperCAmelCase__ :      List[Any])		->Any:





           '''simple docstring'''

           for key, val in cond.items():
                  A__ 					=       val.clone()
           return x_in


    def 			SCREAMING_SNAKE_CASE				(     self :      List[str]   ,					UpperCAmelCase__ :      List[Any]   ,					UpperCAmelCase__ :      List[str]   ,					UpperCAmelCase__ :      Union[str, Any]   ,					UpperCAmelCase__ :      str)		->List[Any]:





           '''simple docstring'''

           A__ 					=       x.shape[0]
           A__ 					=       None
           for i in tqdm.tqdm(self.scheduler.timesteps):
                  # create batch of timesteps to pass into model
                  A__ 					=       torch.full((batch_size,)   ,					UpperCAmelCase__   ,					device=self.unet.device   ,					dtype=torch.long)
                  for _ in range(UpperCAmelCase__):
                         with torch.enable_grad():
                                x.requires_grad_()

                                # permute to match dimension for pre-trained models
                                A__ 					=       self.value_function(x.permute(0   ,					2   ,					1)   ,					UpperCAmelCase__).sample
                                A__ 					=       torch.autograd.grad([y.sum()]   ,					[x])[0]

                                A__ 					=       self.scheduler._get_variance(UpperCAmelCase__)
                                A__ 					=       torch.exp(0.5 * posterior_variance)
                                A__ 					=       model_std * grad

                         A__ 					=       0
                         A__ 					=       x.detach()
                         A__ 					=       x + scale * grad
                         A__ 					=       self.reset_xa(UpperCAmelCase__   ,					UpperCAmelCase__   ,					self.action_dim)

                  A__ 					=       self.unet(x.permute(0   ,					2   ,					1)   ,					UpperCAmelCase__).sample.permute(0   ,					2   ,					1)

                  # TODO: verify deprecation of this kwarg
                  A__ 					=       self.scheduler.step(UpperCAmelCase__   ,					UpperCAmelCase__   ,					UpperCAmelCase__   ,					predict_epsilon=UpperCAmelCase__)['''prev_sample''']

                  # apply conditions to the trajectory (set the initial state)
                  A__ 					=       self.reset_xa(UpperCAmelCase__   ,					UpperCAmelCase__   ,					self.action_dim)
                  A__ 					=       self.to_torch(UpperCAmelCase__)
           return x, y





    def __call__(     self :      Tuple   ,					UpperCAmelCase__ :      List[Any]   ,					UpperCAmelCase__ :      List[Any]=64   ,					UpperCAmelCase__ :      Dict=32   ,					UpperCAmelCase__ :      int=2   ,					UpperCAmelCase__ :      Dict=0.1)		->List[str]:





           '''simple docstring'''

           A__ 					=       self.normalize(UpperCAmelCase__   ,					'''observations''')
           A__ 					=       obs[None].repeat(UpperCAmelCase__   ,					axis=0)

           A__ 					=       {0: self.to_torch(UpperCAmelCase__)}
           A__ 					=       (batch_size, planning_horizon, self.state_dim + self.action_dim)

           # generate initial noise and apply our conditions (to make the trajectories start at current state)
           A__ 					=       randn_tensor(UpperCAmelCase__   ,					device=self.unet.device)
           A__ 					=       self.reset_xa(UpperCAmelCase__   ,					UpperCAmelCase__   ,					self.action_dim)
           A__ 					=       self.to_torch(UpperCAmelCase__)

           # run the diffusion process
           A__					,				A__ 					=       self.run_diffusion(UpperCAmelCase__   ,					UpperCAmelCase__   ,					UpperCAmelCase__   ,					UpperCAmelCase__)

           # sort output trajectories by value
           A__ 					=       y.argsort(0   ,					descending=UpperCAmelCase__).squeeze()
           A__ 					=       x[sorted_idx]
           A__ 					=       sorted_values[:, :, : self.action_dim]
           A__ 					=       actions.detach().cpu().numpy()
           A__ 					=       self.de_normalize(UpperCAmelCase__   ,					key='''actions''')

           # select the action with the highest value
           if y is not None:
                  A__ 					=       0
           else:
                  # if we didn't run value guiding, select a random action
                  A__ 					=       np.random.randint(0   ,					UpperCAmelCase__)

           A__ 					=       denorm_actions[selected_index, 0]
           return denorm_actions








"""simple docstring"""




from typing import Dict

import numpy as np
import torch

from . import residue_constants as rc
from .tensor_utils import tensor_tree_map, tree_map



def        a__					(							snake_case__							)		->   Dict[str, torch.Tensor]:
			lowerCamelCase      =   []
			lowerCamelCase      =   []
			lowerCamelCase      =   []

			for rt in rc.restypes:
						lowerCamelCase      =   rc.restype_name_to_atomaa_names[rc.restype_atoa[rt]]
						restype_atomaa_to_atomaa_list.append([(rc.atom_order[name] if name else 0) for name in atom_names]							)
						lowerCamelCase      =   {name: i for i, name in enumerate(snake_case__							)}
						restype_atomaa_to_atomaa_list.append(
						    [(atom_name_to_idxaa[name] if name in atom_name_to_idxaa else 0) for name in rc.atom_types]							)

						restype_atomaa_mask_list.append([(1.0 if name else 0.0) for name in atom_names]							)

			# Add dummy mapping for restype 'UNK'
			restype_atomaa_to_atomaa_list.append([0] * 14							)
			restype_atomaa_to_atomaa_list.append([0] * 37							)
			restype_atomaa_mask_list.append([0.0] * 14							)

			lowerCamelCase      =   torch.tensor(
			    snake_case__			,					dtype=torch.intaa			,					device=protein["""aatype"""].device			,					)
			lowerCamelCase      =   torch.tensor(
			    snake_case__			,					dtype=torch.intaa			,					device=protein["""aatype"""].device			,					)
			lowerCamelCase      =   torch.tensor(
			    snake_case__			,					dtype=torch.floataa			,					device=protein["""aatype"""].device			,					)
			lowerCamelCase      =   protein["""aatype"""].to(torch.long							)

			# create the mapping for (residx, atom14) --> atom37, i.e. an array
			# with shape (num_res, 14) containing the atom37 indices for this protein
			lowerCamelCase      =   restype_atomaa_to_atomaa[protein_aatype]
			lowerCamelCase      =   restype_atomaa_mask[protein_aatype]

			lowerCamelCase      =   residx_atomaa_mask
			lowerCamelCase      =   residx_atomaa_to_atomaa.long()

			# create the gather indices for mapping back
			lowerCamelCase      =   restype_atomaa_to_atomaa[protein_aatype]
			lowerCamelCase      =   residx_atomaa_to_atomaa.long()

			# create the corresponding mask
			lowerCamelCase      =   torch.zeros([21, 37]			,					dtype=torch.floataa			,					device=protein["""aatype"""].device							)
			for restype, restype_letter in enumerate(rc.restypes							):
						lowerCamelCase      =   rc.restype_atoa[restype_letter]
						lowerCamelCase      =   rc.residue_atoms[restype_name]
						for atom_name in atom_names:
									lowerCamelCase      =   rc.atom_order[atom_name]
									lowerCamelCase      =   1

			lowerCamelCase      =   restype_atomaa_mask[protein_aatype]
			lowerCamelCase      =   residx_atomaa_mask

			return protein



def        a__					(							snake_case__							)		->   Dict[str, np.ndarray]:
			lowerCamelCase      =   tree_map(lambda snake_case__							: torch.tensor(snake_case__			,					device=batch["""aatype"""].device							)			,					snake_case__			,					np.ndarray							)
			lowerCamelCase      =   tensor_tree_map(lambda snake_case__							: np.array(snake_case__							)			,					make_atomaa_masks(snake_case__							)							)
			return out



